How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation

Atsushi Inoue · Lutz Kilian

197 citations0/0 files storedJournal of the American Statistical Association

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Statistical Methods and Inference

Abstract

This article focuses on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean squared error of forecasting models of U.S. consumer price inflation. We propose three variants of the bagging algorithm specifically designed for this type of forecasting problem and evaluate their empirical performance. Although bagging predictors in our application are clearly more accurate than equally weighted forecasts, median forecasts, ARM forecasts, AFTER forecasts, or Bayesian forecast averages based on one extra predictor at a time, they are generally about as accurate as the Bayesian shrinkage predictor, the ridge regression predictor, the iterated LASSO predictor, or the Bayesian model average predictor based on random subsets of extra predictors. Our results show that bagging can achieve large reductions in prediction mean-squared errors even in such challenging applications as inflation forecasting; however, bagging is not the only method capable of achieving such gains.

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References

1969The Combination of Forecasts 2002Forecasting Using Principal Components From a Large Number of Predictors 2002Macroeconomic Forecasting Using Diffusion Indexes 1993Introduction to Multiple Time Series Analysis 1986Forecasting With Bayesian Vector Autoregressions—Five Years of Experience 2003Forecasting Output and Inflation: The Role of Asset Prices 2003Monetary policy in a data-rich environment 2002Stock return predictability and model uncertainty

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